Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

DOI10.1109/TSP.2013.2295553zbMath1394.94973arXiv1303.2221OpenAlexW2059861509MaRDI QIDQ4579005

Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, Nikolai N. Nefedov

Publication date: 22 August 2018

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1303.2221

Mathematics Subject Classification ID

Applications of graph theory to circuits and networks (94C15)

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Manifold Optimization-Assisted Gaussian Variational Approximation ⋮ Mutual structure learning for multiple kernel clustering ⋮ A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging ⋮ A Riemannian Gradient Sampling Algorithm for Nonsmooth Optimization on Manifolds ⋮ A common variable minimax theorem for graphs ⋮ Link prediction in dynamic networks using random dot product graphs ⋮ Unnamed Item ⋮ Nonconvex weak sharp minima on Riemannian manifolds ⋮ Learning robust affinity graph representation for multi-view clustering ⋮ Community detection with dependent connectivity ⋮ Learning a consensus affinity matrix for multi-view clustering via subspaces merging on Grassmann manifold ⋮ Constrained spectral clustering via multi-layer graph embeddings on a Grassmann manifold

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